Now create Pydantic *models* (schemas) that will be used when reading data, when returning it from the API.

For example, before creating an item, we don't know what will be the ID assigned to it, but when reading it (when returning it from the API) we will already know its ID.

The same way, when reading a user, we can now declare that `items` will contain the items that belong to this user.

                Security scheme name.

                It will be included in the generated OpenAPI (e.g. visible at `/docs`).
                """
            ),
        ] = None,
        description: Annotated[
            Optional[str],
            Doc(
                """
                Security scheme description.

                It will be included in the generated OpenAPI (e.g. visible at `/docs`).

Ztunnel will look up the destination from the [addresses](#address-type) it is configured with.

For traffic to unknown addresses, or to workloads that are not a part of the mesh, the traffic will just be passed through as is.
To make ztunnel more transparent, the original source IP address will be spoofed.
Additionally, `splice` will be used to make this proxying more efficient when possible.

You can define logic (code) that should be executed before the application **starts up**. This means that this code will be executed **once**, **before** the application **starts receiving requests**.

The same way, you can define logic (code) that should be executed when the application is **shutting down**. In this case, this code will be executed **once**, **after** having handled possibly **many requests**.

	m.o = m.o[idx:]
}

// mergeEntryChannels will merge entries from in and return them sorted on out.
// To signify no more results are on an input channel, close it.
// The output channel will be closed when all inputs are emptied.
// If file names are equal, compareMeta is called to select which one to choose.
// The entry not chosen will be discarded.
// If the context is canceled the function will return the error,

   * multiset itself).
   *
   * <p>Despite the method name, this method attempts to avoid actually copying the data when it is
   * safe to do so. The exact circumstances under which a copy will or will not be performed are
   * undocumented and subject to change.
   *
   * <p>This method is not type-safe, as it may be called on elements that are not mutually
   * comparable.
   *

   * Optional} will be returned from this method and the iterator will be left exhausted: its {@code
   * hasNext()} method will return {@code false}.
   *
   * <p><b>Warning:</b> avoid using a {@code predicate} that matches {@code null}. If {@code null}
   * is matched in {@code iterator}, a NullPointerException will be thrown.
   *
   * @since 11.0
   */

{!../../../docs_src/wsgi/tutorial001.py!}
```

## Check it

Now, every request under the path `/v1/` will be handled by the Flask application.

And the rest will be handled by **FastAPI**.

If you run it and go to <a href="http://localhost:8000/v1/" class="external-link" target="_blank">http://localhost:8000/v1/</a> you will see the response from Flask:

```txt
Hello, World from Flask!
```

    * This will be done by sending a POST request (from *your API*) to some *external API* provided by that external developer (this is the "callback").

## The normal **FastAPI** app

Let's first see how the normal API app would look like before adding the callback.

It will have a *path operation* that will receive an `Invoice` body, and a query parameter `callback_url` that will contain the URL for the callback.

    ```

Those parameters are what **FastAPI** will use to "solve" the dependency.

In both cases, it will have:

* An optional `q` query parameter that is a `str`.
* A `skip` query parameter that is an `int`, with a default of `0`.
* A `limit` query parameter that is an `int`, with a default of `100`.

In both cases the data will be converted, validated, documented on the OpenAPI schema, etc.

## Use it